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Open AccessArticle

GROF: Indoor Localization Using a Multiple-Bandwidth General Regression Neural Network and Outlier Filter

by Zhang Chen 1,2,* and Jinlong Wang 1
1
College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, China
2
The 63th Institute, National University of Defense Technology, Nanjing 210007, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3723; https://doi.org/10.3390/s18113723
Received: 11 September 2018 / Revised: 25 October 2018 / Accepted: 30 October 2018 / Published: 1 November 2018
(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)
In recent years, a variety of methods have been developed for indoor localization utilizing fingerprints of received signal strength (RSS) that are location dependent. Nevertheless, the RSS is sensitive to environmental variations, in that the resulting fluctuation severely degrades the localization accuracy. Furthermore, the fingerprints survey course is time-consuming and labor-intensive. Therefore, the lightweight fingerprint-based indoor positioning approach is preferred for practical applications. In this paper, a novel multiple-bandwidth generalized regression neural network (GRNN) with the outlier filter indoor positioning approach (GROF) is proposed. The GROF method is based on the GRNN, for which we adopt a new kind of multiple-bandwidth kernel architecture to achieve a more flexible regression performance than that of the traditional GRNN. In addition, an outlier filtering scheme adopting the k-nearest neighbor (KNN) method is embedded into the localization module so as to improve the localization robustness against environmental changes. We discuss the multiple-bandwidth spread value training process and the outlier filtering algorithm, and demonstrate the feasibility and performance of GROF through experiment data, using a Universal Software Radio Peripheral (USRP) platform. The experimental results indicate that the GROF method outperforms the positioning methods, based on the standard GRNN, KNN, or backpropagation neural network (BPNN), both in localization accuracy and robustness, without the extra training sample requirement. View Full-Text
Keywords: fingerprinting; general regression neural network (GRNN); indoor localization; K-nearest-neighbor (KNN); received signal strength (RSS) fingerprinting; general regression neural network (GRNN); indoor localization; K-nearest-neighbor (KNN); received signal strength (RSS)
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Chen, Z.; Wang, J. GROF: Indoor Localization Using a Multiple-Bandwidth General Regression Neural Network and Outlier Filter. Sensors 2018, 18, 3723.

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